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Frontiers of Medicine

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ISSN 2095-0225(Online)

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Front. Med.    2021, Vol. 15 Issue (1) : 125-138    https://doi.org/10.1007/s11684-019-0725-5
RESEARCH ARTICLE
Altered white matter microarchitecture in Parkinson’s disease: a voxel-based meta-analysis of diffusion tensor imaging studies
Xueling Suo1, Du Lei1,2(), Wenbin Li1,2, Lei Li1, Jing Dai3, Song Wang1, Nannan Li4, Lan Cheng4, Rong Peng4, Graham J Kemp5, Qiyong Gong1,6()
1. Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
2. Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, Ohio, USA
3. Department of Psychoradiology, Chengdu Mental Health Center, Chengdu 610041, China
4. Department of Neurology, West China Hospital of Sichuan University, Chengdu 610041, China
5. Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L69 3GE, United Kingdom
6. Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu 610041, China
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Abstract

This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson’s disease (PD) reflected by fractional anisotropy (FA), addressing clinical profiles and methodology-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls (HC) using the anisotropic effect size–signed differential mapping. A total of 808 patients with PD and 760 HC coming from 27 databases were finally included. Subgroup analyses were conducted considering heterogeneity with respect to medication status, disease stage, analysis methods, and the number of diffusion directions in acquisition. Compared with HC, patients with PD had decreased FA in the left middle cerebellar peduncle, corpus callosum (CC), left inferior fronto-occipital fasciculus, and right inferior longitudinal fasciculus. Most of the main results remained unchanged in subgroup meta-analyses of medicated patients, early stage patients, voxel-based analysis, and acquisition with ˂30 diffusion directions. The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex. The cerebellum and CC, associated with typical motor impairment, showed the most consistent FA decreases in PD. Medication status, analysis approaches, and the number of diffusion directions have an important impact on the findings, needing careful evaluation in future meta-analyses.
Keywords Parkinson’s disease      diffusion tensor imaging      fractional anisotropy      meta-analysis      anisotropic effect size–signed differential mapping     
Corresponding Author(s): Du Lei,Qiyong Gong   
Online First Date: 27 May 2020    Issue Date: 11 February 2021
 Cite this article:   
Xueling Suo,Du Lei,Wenbin Li, et al. Altered white matter microarchitecture in Parkinson’s disease: a voxel-based meta-analysis of diffusion tensor imaging studies[J]. Front. Med., 2021, 15(1): 125-138.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-019-0725-5
https://academic.hep.com.cn/fmd/EN/Y2021/V15/I1/125
Fig.1  Flowchart describing the study selection for the meta-analysis.
Study Sample (female) Age (year) UPDRS-III H&Y stage Duration (year) Medication status LEDD (mg/day) Scanner No. of directions Software Methods Threshold No. of coordinates Quality scores (out of 12)
PD HC PD HC
Guan et al. (2019) [30] 65 (33) 46 (25) 55.5 57.8 27.1 2.3 4.7 Off-state NA 3.0T 15 FSL TBSS P ＜ 0.001, cluster-based corr 1 11
Wen et al. (2018) [31] 13 (3) 61
(20)		 66.66 60.19 22.46 1.69 0.55 Drug-naïve − 3.0T 64 FSL TBSS P ＜ 0.05, TFCE corr 0 10.5
Peran et al. (2018) [32] 26 (14) 26 (15) 63.8 66.0 19.1 ＜4 7.4 On-state 689 3.0T 32 FSL VBA P ＜ 0.01, TFCE corr 0 12
Rektor et al. (2018) [33] 20
(9)		 21 (13) 61.9 57.9 NA 1–1.5 ≤5 On-state NA 3.0T 60 FSL TBSS P ＜ 0.05, TFCE corr 0 11.5
Acosta-Cabronero et al. (2017) [34] 25
(5)		 50 (22) 63.6 63.6 16.3 2.2 6.0 On-state 748 3.0T 30 FSL TBSS P ＜ 0.001, uncorr 0 10.5
Chen et al. (2017) [6] 18 (11) 24 (13) 62.28 62.88 17.39 NA 3.06 Off-state NA 3.0T 25 FSL TBSS P ＜ 0.017, TFCE & Bonferroni corr 2 11
Chen et al. (2017) [11] 29 (9) 26 (7) 61.51 60.11 27.03 2.36 NA Off-state NA 3.0T 13 FSL VBA P ＜ 0.05, AlphaSim corr 3 11
Chiang et al. (2017) [10] 66 (43) 67 (38) 58.1 56.8 22.74 1.98 3.86 On-state 279 3.0T 13 FSL VBA P ＜ 0.05, 3dClusterSim corr 6 11
Kamagata et al. (2017) [42] 30 (18) 28 (18) 67.6 66.5 16.1* 2.1 6.4 On-state NA 3.0T 32 FSL GBSS P ＜ 0.05, TFCE corr 2 11.5
Luo et al. (2017) [13] 301 (14) 26 (13) 53.42 54.46 25.37 1.60 2.00 Off-state 262 3.0T 25 FSL TBSS P ＜ 0.05, TFCE corr 0 11
302 (15) 26 (13) 52.55 54.46 22.27 1.63 2.35 Off-state 305 3.0T 25 FSL TBSS P ＜ 0.05, TFCE corr 0 11
Zanigni et al. (2017) [35] 47 (15) 27 (15) 66.5* 55.0* NA 2.5* 2.8* NA NA 1.5T 25 FSL TBSS P ＜ 0.0038, TFCE & Bonferroni corr 0 10
Vervoort et al. (2016) [36] 16 (7) 19 (5) 55.1 58.1 28.9 1.94 4.87 Off-state 249 3.0T 61 FSL TBSS P ＜ 0.05, TFCE corr 0 10.5
Ji et al. (2015) [7] 20 (9) 20 (10) 64.20 59.95 32* 2* 5* Off-state NA 3.0T 30 FSL TBSS P ＜ 0.05, TFCE corr 1 11
Vercruysse et al. (2015) [8] 113 (3) 15 (4) 68.6 68.1 36.6 3* 9.5 On-state 704 3.0T 25/40/75 FSL TBSS P ＜ 0.05, FDR corr 1 11.5
154 (4) 15 (4) 67.6 68.1 32.5 2.5* 7.6 On-state 461 3.0T 25/40/75 FSL TBSS P ＜ 0.05, FDR corr 3 11.5
Agosta et al. (2014) [37] 13 (7) 33 (16) 63.9 64.0 28.3 2.4 10.0 On-state 567 1.5T 12 FSL TBSS P ＜ 0.05, TFCE corr 0 9.5
Worker et al. (2014) [14] 14 (7) 17 (8) 64.7 63.9 21.8 2.5* 6.6 On-state NA 3.0T 64 FSL TBSS P ＜ 0.0167, TFCE & Bonferroni corr 0 11
Rosskopf et al. (2014) [43] 15
(4)		 18
(5)		 67* 66* 26# NA 4* NA NA 1.5T 12 TIFT WBSS P ＜ 0.05, FDR corr 1 9.5
Agosta et al. (2013) [15] 635 (22) 42 (17) 62.54 64 22.30 1–2.5 5.65 Off-state NA 1.5T 12 FSL TBSS P ＜ 0.05, TFCE corr 0 9.5
266 (12) 42 (17) 65 64 40.92 3.46 12.38 Off-state NA 1.5T 12 FSL TBSS P ＜ 0.05, TFCE corr 0 9.5
Kamagata et al. (2013) [38] 20 (12) 20 (10) 71.6 72.7 NA 2.4 7.83 On-state 464* 3.0T 32 FSL TBSS P ＜ 0.05, TFCE corr 0 10.5
Kim et al. (2013) [39] 64 (42) 64 (42) 62.9 63.0 NA 2* 5.3 Off-state NA 3.0T 15 FSL TBSS P ＜ 0.05, TFCE corr 0 9.5
Melzer et al. (2013) [40] 63 (20) 32 (10) 64.0 70.1 25.3 2* 3.7 On-state 208 3.0T 28 FSL TBSS P ＜ 0.05, TFCE corr 0 11
Hattori et al. (2012) [41] 32 (20) 40 (22) 75.9 76.9 20 2.7 5.8 NA NA 1.5T 12 FSL TBSS P ＜ 0.05, TFCE corr 0 9.5
Zhang et al. (2011) [12] 25 (14) 25 (14) 58.4 58.4 48# 1–3 6.44 On-state NA 3.0T 12 FSL VBA P ＜ 0.05, cluster-based corr 3 10.5
Karagulle Kendi et al. (2008) [9] 12 (7) 13 (5) 62.1 58.0 43.7# 1.8 5.8 On-state 585 3.0T 12 SPM VBA P ＜ 0.05, corr for multiple comparison 18 11
Tab.1  Demographic and clinical characteristics of participants in the 24 PD studies (27 data sets) included in the meta-analysis
Fig.2  Regions showing reduced fractional anisotropy in patients with Parkinson’s disease compared with healthy controls. CC, corpus callosum; IFOF, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; L, left; MCP, middle cerebellar peduncles; R, right.
Brain region (PD ＜ HC) MNI coordinates SDM Z score P value (uncorrected) No. of voxels Cluster breakdown (no. of voxels) Egger’s test (P value)
X Y Z
L middle cerebellar peduncles −32 −54 −42 −1.412 0.000098109 221 Middle cerebellar peduncles (84) 0.107
L cerebellum, crus I (9)
L cerebellum, hemispheric lobule VIIB (7)
L cerebellum, hemispheric lobule VIII (7)
L cerebellum, crus II (5)
L cerebellum, hemispheric lobule VI (1)
Undefined (108)
L corpus callosum −22 −62 34 −1.754 ~0 87 Corpus callosum (66) 0.122
L superior longitudinal fasciculus II (11)
L superior longitudinal fasciculus I (4)
L inferior parietal (excluding supramarginal and angular) gyri, BA 7 (3)
L middle occipital gyrus, BA 7 (3)
L inferior network, inferior fronto-occipital fasciculus −36 −20 −4 −1.164 0.000784993 59 L inferior network, inferior fronto-occipital fasciculus (43) 0.126
Undefined, BA 48 (12)
Undefined, BA 20 (4)
R corpus callosum 26 −60 32 −1.251 0.000539660 29 Corpus callosum (29) 0.059
R inferior network, inferior longitudinal fasciculus 40 −58 −4 −1.204 0.000637770 16 R inferior network, inferior longitudinal fasciculus (12) 0.057
Right inferior temporal gyrus, BA 37 (4)
Tab.2  Regional FA differences between patients with PD and HC identified by the present meta-analysis
Discarded study Left middle cerebellar peduncles Left corpus callosum Right corpus callosum Left inferior network, inferior fronto-occipital fasciculus Right inferior network, inferior longitudinal fasciculus
Guan et al. (2019) [30] Yes Yes Yes Yes Yes
Wen et al. (2018) [31] Yes Yes Yes Yes Yes
Peran et al. (2018) [32] Yes Yes Yes Yes Yes
Rektor et al. (2018) [33] Yes Yes Yes Yes Yes
Acosta-Cabronero et al. (2017) [34] Yes Yes Yes Yes Yes
Chen et al. (2017) [6] Yes Yes Yes Yes Yes
Chen et al. (2017) [11] Yes Yes Yes Yes Yes
Chiang et al. (2017) [10] Yes Yes No No No
Kamagata et al. (2017) [42] Yes Yes Yes No Yes
Luo et al. (2017) a [13] Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes
Zanigni et al. (2017) [35] Yes Yes Yes Yes Yes
Vervoort et al. (2016) [36] Yes Yes Yes Yes Yes
Ji et al. (2015) [7] Yes Yes Yes Yes Yes
Vercruysse et al. (2015) a [8] Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes
Agosta et al. (2014) [37] Yes Yes Yes Yes Yes
Worker et al. (2014) [14] Yes Yes Yes Yes Yes
Rosskopf et al. (2014) [43] Yes Yes Yes Yes Yes
Agosta et al. (2013) a [15] Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes
Kamagata et al. (2013) [38] Yes Yes Yes Yes Yes
Kim et al. (2013) [39] Yes Yes Yes Yes Yes
Melzer et al. (2013) [40] Yes Yes Yes Yes Yes
Hattori et al. (2012) [41] Yes Yes Yes Yes Yes
Zhang et al. (2011) [12] Yes Yes Yes Yes Yes
Karagulle Kendi et al. (2008) [9] Yes Yes Yes Yes Yes
Total 27 out of 27 27 out of 27 26 out of 27 25 out of 27 26 out of 27
Tab.3  Jackknife sensitivity analysis*
Brain region MNI coordinates SDM Z score P value (uncorrected) No. of voxels Cluster breakdown
(no. of voxels)
X Y Z
Corpus callosum −26 −62 32 1.911 0.000073612 12 Corpus callosum (12)
Tab.4  Regions of FA heterogeneity from the SDM analysis
Brain region (PD<HC) MNI coordinates SDMZ score P value
(uncorrected)		 No. of voxels
X Y Z
Medication-free patients R olfactory cortex, BA 48 22 12 −18 −1.156 0.000049055 50
Medicated patients L middle cerebellar peduncles −34 −54 −44 −1.645 0.000296830 65
L corpus callosum −24 −62 28 −1.647 0.000220776 61
L inferior network, inferior fronto-occipital fasciculus −36 −20 −4 −1.662 0.000196218 54
R corpus callosum 24 −60 32 −1.645 0.000269830 31
R inferior network, inferior longitudinal fasciculus 42 −58 −6 −1.630 0.000318885 17
LEDD≥400 mg R corpus callosum 4 −14 26 1.122 0.000049055 146
L corpus callosum −22 −38 28 1.060 0.000343442 65
LEDD < 400 mg L middle cerebellar peduncles −30 −54 −38 −1.496 0.000073612 71
R corpus callosum 28 −62 28 −1.495 0.000073612 31
L corpus callosum −20 −62 32 −1.457 0.000392497 23
L inferior network, inferior fronto-occipital fasciculus −34 −20 −2 −1.486 0.000171721 21
R inferior network, inferior longitudinal fasciculus 44 −54 −6 −1.481 0.000269830 12
Early stage patients L middle cerebellar peduncles −34 −58 −38 −1.473 0.000024557 76
R corpus callosum 22 −60 34 −1.467 0.000098109 31
L middle occipital gyrus, BA 7 −30 −68 38 −1.458 0.000196218 30
L corpus callosum −20 −60 34 −1.465 0.000122666 20
L inferior network, inferior fronto-occipital fasciculus −36 −20 −4 −1.445 0.000269830 15
R inferior network, inferior longitudinal fasciculus 42 −64 −6 −1.458 0.000196218 12
TBSS None
VBA L middle cerebellar peduncles −40 −56 −38 −2.350 ~0 263
L corpus callosum −28 −64 26 −2.341 ~0 97
R corpus callosum 24 −58 30 −1.721 0.000515163 24
L inferior network, inferior fronto-occipital fasciculus −36 −20 −4 −1.667 0.001030266 12
Right inferior network, inferior longitudinal fasciculus 44 −56 −6 −1.702 0.000662327 10
Number of diffusion directions≥30 −36 −14 −10 −1.133 0.000073612 66
Left inferior network, inferior fronto-occipital fasciculus
Number of diffusion directions<30 −32 −54 −42 −1.682 0.000024557 224
L middle cerebellar peduncles
L corpus callosum −28 −62 28 −1.683 0.000024557 87
R corpus callosum 26 −60 30 −1.357 0.000343442 29
R inferior network, inferior longitudinal fasciculus 40 −56 −4 −1.334 0.000539660 16
Left inferior network, inferior fronto-occipital fasciculus −36 −20 −4 −1.023 0.002551138 10
Tab.5  Subgroup meta-analysis of studies in patients with PD compared with HC
1 LM de Lau, MM Breteler. Epidemiology of Parkinson’s disease. Lancet Neurol 2006; 5(6): 525–535
https://doi.org/10.1016/S1474-4422(06)70471-9 pmid: 16713924
2 H Reichmann, MD Brandt, L Klingelhoefer. The nonmotor features of Parkinson’s disease: pathophysiology and management advances. Curr Opin Neurol 2016; 29(4): 467–473
https://doi.org/10.1097/WCO.0000000000000348 pmid: 27262147
3 M Kubicki, CF Westin, SE Maier, H Mamata, M Frumin, H Ersner-Hershfield, R Kikinis, FA Jolesz, R McCarley, ME Shenton. Diffusion tensor imaging and its application to neuropsychiatric disorders. Harv Rev Psychiatry 2002; 10(6): 324–336
https://doi.org/10.1080/10673220216231 pmid: 12485979
4 WD Taylor, E Hsu, KR Krishnan, JR MacFall. Diffusion tensor imaging: background, potential, and utility in psychiatric research. Biol Psychiatry 2004; 55(3): 201–207
https://doi.org/10.1016/j.biopsych.2003.07.001 pmid: 14744459
5 D Le Bihan, JF Mangin, C Poupon, CA Clark, S Pappata, N Molko, H Chabriat. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001; 13(4): 534–546
https://doi.org/10.1002/jmri.1076 pmid: 11276097
6 B Chen, G Fan, W Sun, X Shang, S Shi, S Wang, G Lv, C. Wu Usefulness of diffusion-tensor MRI in the diagnosis of Parkinson variant of multiple system atrophy and Parkinson’s disease: a valuable tool to differentiate between them? Clin Radiol 2017; 72(7): 610.e9–610.e15
https://doi.org/10.1016/j.crad.2017.02.005 pmid: 28318507
7 L Ji, Y Wang, D Zhu, W Liu, J Shi. White matter differences between multiple system atrophy (parkinsonian type) and Parkinson’s disease: a diffusion tensor image study. Neuroscience 2015; 305: 109–116
https://doi.org/10.1016/j.neuroscience.2015.07.060 pmid: 26215920
8 S Vercruysse, I Leunissen, G Vervoort, W Vandenberghe, S Swinnen, A Nieuwboer. Microstructural changes in white matter associated with freezing of gait in Parkinson’s disease. Mov Disord 2015; 30(4): 567–576
https://doi.org/10.1002/mds.26130 pmid: 25640958
9 AT Karagulle Kendi, S Lehericy, M Luciana, K Ugurbil, P Tuite. Altered diffusion in the frontal lobe in Parkinson disease. AJNR Am J Neuroradiol 2008; 29(3): 501–505
https://doi.org/10.3174/ajnr.A0850 pmid: 18202242
10 PL Chiang, HL Chen, CH Lu, PC Chen, MH Chen, IH Yang, NW Tsai, WC Lin. White matter damage and systemic inflammation in Parkinson’s disease. BMC Neurosci 2017; 18(1): 48
https://doi.org/10.1186/s12868-017-0367-y pmid: 28595572
11 MH Chen, PC Chen, CH Lu, HL Chen, YP Chao, SH Li, YW Chen, WC Lin. Plasma DNA mediate autonomic dysfunctions and white matter injuries in patients with Parkinson’s disease. Oxid Med Cell Longev 2017; 2017: 7371403
https://doi.org/10.1155/2017/7371403 pmid: 28232858
12 K Zhang, C Yu, Y Zhang, X Wu, C Zhu, P Chan, K Li. Voxel-based analysis of diffusion tensor indices in the brain in patients with Parkinson’s disease. Eur J Radiol 2011; 77(2): 269–273
https://doi.org/10.1016/j.ejrad.2009.07.032 pmid: 19692193
13 C Luo, W Song, Q Chen, J Yang, Q Gong, HF Shang. White matter microstructure damage in tremor-dominant Parkinson’s disease patients. Neuroradiology 2017; 59(7): 691–698
https://doi.org/10.1007/s00234-017-1846-7 pmid: 28540401
14 A Worker, C Blain, J Jarosz, KR Chaudhuri, GJ Barker, SC Williams, RG Brown, PN Leigh, F Dell’Acqua, A Simmons. Diffusion tensor imaging of Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy: a tract-based spatial statistics study. PLoS One 2014; 9(11): e112638
https://doi.org/10.1371/journal.pone.0112638 pmid: 25405990
15 F Agosta, E Canu, T Stojković, M Pievani, A Tomić, L Sarro, N Dragašević, M Copetti, G Comi, VS Kostić, M Filippi. The topography of brain damage at different stages of Parkinson’s disease. Hum Brain Mapp 2013; 34(11): 2798–2807
https://doi.org/10.1002/hbm.22101 pmid: 22528144
16 ST Schwarz, M Abaei, V Gontu, PS Morgan, N Bajaj, DP Auer. Diffusion tensor imaging of nigral degeneration in Parkinson’s disease: a region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. Neuroimage Clin 2013; 3: 481–488
https://doi.org/10.1016/j.nicl.2013.10.006 pmid: 24273730
17 C Atkinson-Clement, S Pinto, A Eusebio, O Coulon. Diffusion tensor imaging in Parkinson’s disease: review and meta-analysis. Neuroimage Clin 2017; 16: 98–110
https://doi.org/10.1016/j.nicl.2017.07.011 pmid: 28765809
18 CJ Cochrane, KP Ebmeier. Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology 2013; 80(9): 857–864
https://doi.org/10.1212/WNL.0b013e318284070c pmid: 23439701
19 F Albrecht, T Ballarini, J Neumann, ML Schroeter. FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson’s disease: a whole-brain multimodal imaging meta-analysis. Neuroimage Clin 2019; 21: 101594
https://doi.org/10.1016/j.nicl.2018.11.004 pmid: 30514656
20 D Moher, A Liberati, J Tetzlaff, DG Altman; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6(7): e1000097
https://doi.org/10.1371/journal.pmed.1000097 pmid: 19621072
21 P Pan, H Zhan, M Xia, Y Zhang, D Guan, Y Xu. Aberrant regional homogeneity in Parkinson’s disease: a voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies. Neurosci Biobehav Rev 2017; 72: 223–231
https://doi.org/10.1016/j.neubiorev.2016.11.018 pmid: 27916710
22 AM Shepherd, SL Matheson, KR Laurens, VJ Carr, MJ Green. Systematic meta-analysis of insula volume in schizophrenia. Biol Psychiatry 2012; 72(9): 775–784
https://doi.org/10.1016/j.biopsych.2012.04.020 pmid: 22621997
23 J Radua, D Mataix-Cols. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry 2009; 195(5): 393–402
https://doi.org/10.1192/bjp.bp.108.055046 pmid: 19880927
24 J Radua, D Mataix-Cols, ML Phillips, W El-Hage, DM Kronhaus, N Cardoner, S Surguladze. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur Psychiatry 2012; 27(8): 605–611
https://doi.org/10.1016/j.eurpsy.2011.04.001 pmid: 21658917
25 J Radua, K Rubia, EJ Canales-Rodríguez, E Pomarol-Clotet, P Fusar-Poli, D Mataix-Cols. Anisotropic kernels for coordinate-based meta-analyses of neuroimaging studies. Front Psychiatry 2014; 5: 13
https://doi.org/10.3389/fpsyt.2014.00013 pmid: 24575054
26 T Wise, J Radua, G Nortje, AJ Cleare, AH Young, D Arnone. Voxel-based meta-analytical evidence of structural disconnectivity in major depression and bipolar disorder. Biol Psychiatry 2016; 79(4): 293–302
https://doi.org/10.1016/j.biopsych.2015.03.004 pmid: 25891219
27 J Radua, M Grau, OA van den Heuvel, M Thiebaut de Schotten, DJ Stein, EJ Canales-Rodríguez, M Catani, D Mataix-Cols. Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder. Neuropsychopharmacology 2014; 39(7): 1547–1557
https://doi.org/10.1038/npp.2014.5 pmid: 24407265
28 S Chen, P Chan, S Sun, H Chen, B Zhang, W Le, C Liu, G Peng, B Tang, L Wang, Y Cheng, M Shao, Z Liu, Z Wang, X Chen, M Wang, X Wan, H Shang, Y Liu, P Xu, J Wang, T Feng, X Chen, X Hu, A Xie, Q Xiao. The recommendations of Chinese Parkinson’s disease and movement disorder society consensus on therapeutic management of Parkinson’s disease. Transl Neurodegener 2016; 5(1): 12
https://doi.org/10.1186/s40035-016-0059-z pmid: 27366321
29 DK Jones. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn Reson Med 2004; 51(4): 807–815
https://doi.org/10.1002/mrm.20033 pmid: 15065255
30 X Guan, P Huang, Q Zeng, C Liu, H Wei, M Xuan, Q Gu, X Xu, N Wang, X Yu, X Luo, M Zhang. Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson’s disease. Brain Imaging Behav 2019; 13(1): 220–231
https://doi.org/10.1007/s11682-018-9842-z pmid: 29417492
31 MC Wen, HSE Heng, Z Lu, Z Xu, LL Chan, EK Tan, LCS Tan. Differential white matter regional alterations in motor subtypes of early drug-naive Parkinson’s disease patients. Neurorehabil Neural Repair 2018; 32(2): 129–141
https://doi.org/10.1177/1545968317753075 pmid: 29347868
32 P Péran, G Barbagallo, F Nemmi, M Sierra, M Galitzky, AP Traon, P Payoux, WG Meissner, O Rascol. MRI supervised and unsupervised classification of Parkinson’s disease and multiple system atrophy. Mov Disord 2018; 33(4): 600–608
https://doi.org/10.1002/mds.27307 pmid: 29473662
33 I Rektor, A Svátková, L Vojtíšek, I Zikmundová, J Vaníček, A Király, N Szabó. White matter alterations in Parkinson’s disease with normal cognition precede grey matter atrophy. PLoS One 2018; 13(1): e0187939
https://doi.org/10.1371/journal.pone.0187939 pmid: 29304183
34 J Acosta-Cabronero, A Cardenas-Blanco, MJ Betts, M Butryn, JP Valdes-Herrera, I Galazky, PJ Nestor. The whole-brain pattern of magnetic susceptibility perturbations in Parkinson’s disease. Brain 2017; 140(1): 118–131
https://doi.org/10.1093/brain/aww278 pmid: 27836833
35 S Zanigni, S Evangelisti, C Testa, DN Manners, G Calandra-Buonaura, M Guarino, A Gabellini, LL Gramegna, G Giannini, L Sambati, P Cortelli, R Lodi, C Tonon. White matter and cortical changes in atypical parkinsonisms: a multimodal quantitative MR study. Parkinsonism Relat Disord 2017; 39: 44–51
https://doi.org/10.1016/j.parkreldis.2017.03.001 pmid: 28291592
36 G Vervoort, I Leunissen, M Firbank, E Heremans, E Nackaerts, W Vandenberghe, A Nieuwboer. Structural brain alterations in motor subtypes of Parkinson’s disease: evidence from probabilistic tractography and shape analysis. PLoS One 2016; 11(6): e0157743
https://doi.org/10.1371/journal.pone.0157743 pmid: 27314952
37 F Agosta, E Canu, E Stefanova, L Sarro, A Tomić, V Špica, G Comi, VS Kostić, M Filippi. Mild cognitive impairment in Parkinson’s disease is associated with a distributed pattern of brain white matter damage. Hum Brain Mapp 2014; 35(5): 1921–1929
https://doi.org/10.1002/hbm.22302 pmid: 23843285
38 K Kamagata, Y Motoi, H Tomiyama, O Abe, K Ito, K Shimoji, M Suzuki, M Hori, A Nakanishi, T Sano, R Kuwatsuru, K Sasai, S Aoki, N Hattori. Relationship between cognitive impairment and white-matter alteration in Parkinson’s disease with dementia: tract-based spatial statistics and tract-specific analysis. Eur Radiol 2013; 23(7): 1946–1955
https://doi.org/10.1007/s00330-013-2775-4 pmid: 23404139
39 HJ Kim, SJ Kim, HS Kim, CG Choi, N Kim, S Han, EH Jang, SJ Chung, CS Lee. Alterations of mean diffusivity in brain white matter and deep gray matter in Parkinson’s disease. Neurosci Lett 2013; 550: 64–68
https://doi.org/10.1016/j.neulet.2013.06.050 pmid: 23831353
40 TR Melzer, R Watts, MR MacAskill, TL Pitcher, L Livingston, RJ Keenan, JC Dalrymple-Alford, TJ Anderson. White matter microstructure deteriorates across cognitive stages in Parkinson disease. Neurology 2013; 80(20): 1841–1849
https://doi.org/10.1212/WNL.0b013e3182929f62 pmid: 23596076
41 T Hattori, S Orimo, S Aoki, K Ito, O Abe, A Amano, R Sato, K Sakai, H Mizusawa. Cognitive status correlates with white matter alteration in Parkinson’s disease. Hum Brain Mapp 2012; 33(3): 727–739
https://doi.org/10.1002/hbm.21245 pmid: 21495116
42 K Kamagata, A Zalesky, T Hatano, R Ueda, MA Di Biase, A Okuzumi, K Shimoji, M Hori, K Caeyenberghs, C Pantelis, N Hattori, S Aoki. Gray matter abnormalities in idiopathic Parkinson’s disease: evaluation by diffusional kurtosis imaging and neurite orientation dispersion and density imaging. Hum Brain Mapp 2017; 38(7): 3704–3722
https://doi.org/10.1002/hbm.23628 pmid: 28470878
43 J Rosskopf, HP Müller, HJ Huppertz, AC Ludolph, EH Pinkhardt, J Kassubek. Frontal corpus callosum alterations in progressive supranuclear palsy but not in Parkinson’s disease. Neurodegener Dis 2014; 14(4): 184–193
https://doi.org/10.1159/000367693 pmid: 25377379
44 E Ziegler, M Rouillard, E André, T Coolen, J Stender, E Balteau, C Phillips, G Garraux. Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson’s disease. Neuroimage 2014; 99: 498–508
https://doi.org/10.1016/j.neuroimage.2014.06.033 pmid: 24956065
45 CJ Stoodley, JD Schmahmann. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 2010; 46(7): 831–844
https://doi.org/10.1016/j.cortex.2009.11.008 pmid: 20152963
46 T Wu, M Hallett. The cerebellum in Parkinson’s disease. Brain 2013; 136(3): 696–709
https://doi.org/10.1093/brain/aws360 pmid: 23404337
47 PM Schweder, PC Hansen, AL Green, G Quaghebeur, J Stein, TZ Aziz. Connectivity of the pedunculopontine nucleus in parkinsonian freezing of gait. Neuroreport 2010; 21(14): 914–916
https://doi.org/10.1097/WNR.0b013e32833ce5f1 pmid: 20729769
48 E Canu, F Agosta, E Sarasso, MA Volontè, S Basaia, T Stojkovic, E Stefanova, G Comi, A Falini, VS Kostic, R Gatti, M Filippi. Brain structural and functional connectivity in Parkinson’s disease with freezing of gait. Hum Brain Mapp 2015; 36(12): 5064–5078
https://doi.org/10.1002/hbm.22994 pmid: 26359798
49 C Tessa, C Lucetti, S Diciotti, L Paoli, P Cecchi, M Giannelli, F Baldacci, A Ginestroni, C Vignali, M Mascalchi, U Bonuccelli. Hypoactivation of the primary sensorimotor cortex in de novo Parkinson’s disease: a motor fMRI study under controlled conditions. Neuroradiology 2012; 54(3): 261–268
https://doi.org/10.1007/s00234-011-0955-y pmid: 21927866
50 JM Hall, KA Ehgoetz Martens, CC Walton, C O’Callaghan, PE Keller, SJ Lewis, AA Moustafa. Diffusion alterations associated with Parkinson’s disease symptomatology: a review of the literature. Parkinsonism Relat Disord 2016; 33: 12–26
https://doi.org/10.1016/j.parkreldis.2016.09.026 pmid: 27765426
51 S Sobhani, F Rahmani, MH Aarabi, AV Sadr. Exploring white matter microstructure and olfaction dysfunction in early Parkinson disease: diffusion MRI reveals new insight. Brain Imaging Behav 2019; 13(1): 210–219
https://doi.org/10.1007/s11682-017-9781-0 pmid: 29134611
52 M Catani, RJ Howard, S Pajevic, DK Jones. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 2002; 17(1): 77–94
https://doi.org/10.1006/nimg.2002.1136 pmid: 12482069
53 A Cronin-Golomb. Parkinson’s disease as a disconnection syndrome. Neuropsychol Rev 2010; 20(2): 191–208
https://doi.org/10.1007/s11065-010-9128-8 pmid: 20383586
54 X Hu, J Zhang, X Jiang, C Zhou, L Wei, X Yin, Y Wu, J Li, Y Zhang, J Wang. Decreased interhemispheric functional connectivity in subtypes of Parkinson’s disease. J Neurol 2015; 262(3): 760–767
https://doi.org/10.1007/s00415-014-7627-x pmid: 25577177
55 C Luo, X Guo, W Song, B Zhao, B Cao, J Yang, Q Gong, HF Shang. Decreased resting-state interhemispheric functional connectivity in Parkinson’s disease. BioMed Res Int 2015; 2015: 692684
https://doi.org/10.1155/2015/692684 pmid: 26180807
56 J Li, Y Yuan, M Wang, J Zhang, L Zhang, S Jiang, X Wang, J Ding, K Zhang. Decreased interhemispheric homotopic connectivity in Parkinson’s disease patients with freezing of gait: a resting state fMRI study. Parkinsonism Relat Disord 2018; 52: 30–36
https://doi.org/10.1016/j.parkreldis.2018.03.015 pmid: 29602542
57 Z Zheng, S Shemmassian, C Wijekoon, W Kim, SY Bookheimer, N Pouratian. DTI correlates of distinct cognitive impairments in Parkinson’s disease. Hum Brain Mapp 2014; 35(4): 1325–1333
https://doi.org/10.1002/hbm.22256 pmid: 23417856
58 M Gorges, HP Müller, I Liepelt-Scarfone, A Storch, R Dodel; Consortium LANDSCAPE, R Hilker-Roggendorf , D Berg, MS Kunz, E Kalbe, S Baudrexel, J Kassubek, J Kassubek. Structural brain signature of cognitive decline in Parkinson’s disease: DTI-based evidence from the LANDSCAPE study. Ther Adv Neurol Disorder 2019; 12: 1756286419843447
https://doi.org/10.1177/1756286419843447 pmid: 31205489
59 LF Vasconcellos, JS Pereira, M Adachi, D Greca, M Cruz, AL Malak, H Charchat-Fichman. Volumetric brain analysis as a predictor of a worse cognitive outcome in Parkinson’s disease. J Psychiatr Res 2018; 102: 254–260
https://doi.org/10.1016/j.jpsychires.2018.04.016 pmid: 29729620
60 LL Chan, KM Ng, H Rumpel, S Fook-Chong, HH Li, EK Tan. Transcallosal diffusion tensor abnormalities in predominant gait disorder parkinsonism. Parkinsonism Relat Disord 2014; 20(1): 53–59
https://doi.org/10.1016/j.parkreldis.2013.09.017 pmid: 24126023
61 S Galantucci, F Agosta, I Stankovic, I Petrovic, T Stojkovic, V Kostic, M Filippi. Corpus callosum damage and motor function in Parkinson’s disease (P2.006). Neurology 2014; 82(10 Supplement):P2.006
62 M Catani, DK Jones, R Donato, DH Ffytche. Occipito-temporal connections in the human brain. Brain 2003; 126(9): 2093–2107
https://doi.org/10.1093/brain/awg203 pmid: 12821517
63 CJ Fox, G Iaria, JJ Barton. Disconnection in prosopagnosia and face processing. Cortex 2008; 44(8): 996–1009
https://doi.org/10.1016/j.cortex.2008.04.003 pmid: 18597749
64 DH Ffytche. The hodology of hallucinations. Cortex 2008; 44(8): 1067–1083
https://doi.org/10.1016/j.cortex.2008.04.005 pmid: 18586234
65 ED Ross. Sensory-specific amnesia and hypoemotionality in humans and monkeys: gateway for developing a hodology of memory. Cortex 2008; 44(8): 1010–1022
https://doi.org/10.1016/j.cortex.2008.02.002 pmid: 18585698
66 M Catani. From hodology to function. Brain 2007; 130(3): 602–605
https://doi.org/10.1093/brain/awm008 pmid: 17322561
67 M Catani, M Mesulam. The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 2008; 44(8): 953–961
https://doi.org/10.1016/j.cortex.2008.04.002 pmid: 18614162
68 D Rudrauf, S Mehta, TJ Grabowski. Disconnection’s renaissance takes shape: formal incorporation in group-level lesion studies. Cortex 2008; 44(8): 1084–1096
https://doi.org/10.1016/j.cortex.2008.05.005 pmid: 18625495
69 M Haghshomar, M Dolatshahi, F Ghazi Sherbaf, H Sanjari Moghaddam, M Shirin Shandiz, MH Aarabi. Disruption of inferior longitudinal fasciculus microstructure in Parkinson’s disease: a systematic review of diffusion tensor imaging studies. Front Neurol 2018; 9: 598
https://doi.org/10.3389/fneur.2018.00598 pmid: 30093877
70 E Lee, JE Lee, K Yoo, JY Hong, J Oh, MK Sunwoo, JS Kim, Y Jeong, PH Lee, YH Sohn, SY Kang. Neural correlates of progressive reduction of bradykinesia in de novo Parkinson’s disease. Parkinsonism Relat Disord 2014; 20(12): 1376–1381
https://doi.org/10.1016/j.parkreldis.2014.09.027 pmid: 25304859
71 M Wang, S Jiang, Y Yuan, L Zhang, J Ding, J Wang, J Zhang, K Zhang, J Wang. Alterations of functional and structural connectivity of freezing of gait in Parkinson’s disease. J Neurol 2016; 263(8): 1583–1592
https://doi.org/10.1007/s00415-016-8174-4 pmid: 27230857
72 SYZ Tan, NCH Keong, RMP Selvan, H Li, LQR Ooi, EK Tan, LL Chan. Periventricular white matter abnormalities on diffusion tensor imaging of postural instability gait disorder parkinsonism. AJNR Am J Neuroradiol 2019; 40(4): 609–613
https://doi.org/10.3174/ajnr.A5993 pmid: 30872421
73 JY Wu, Y Zhang, WB Wu, G Hu, Y Xu. Impaired long contact white matter fibers integrity is related to depression in Parkinson’s disease. CNS Neurosci Ther 2018; 24(2): 108–114
https://doi.org/10.1111/cns.12778 pmid: 29125694
74 GW Duncan, MJ Firbank, AJ Yarnall, TK Khoo, DJ Brooks, RA Barker, DJ Burn, JT O’Brien. Gray and white matter imaging: a biomarker for cognitive impairment in early Parkinson’s disease? Mov Disord 2016; 31(1): 103–110
https://doi.org/10.1002/mds.26312 pmid: 26202802
75 JS Reijnders, U Ehrt, WE Weber, D Aarsland, AF Leentjens. A systematic review of prevalence studies of depression in Parkinson’s disease. Mov Disord 2008; 23(2): 183–189, quiz 313
https://doi.org/10.1002/mds.21803 pmid: 17987654
76 J Yu, CLM Lam, TMC Lee. White matter microstructural abnormalities in amnestic mild cognitive impairment: a meta-analysis of whole-brain and ROI-based studies. Neurosci Biobehav Rev 2017; 83: 405–416
https://doi.org/10.1016/j.neubiorev.2017.10.026 pmid: 29092777
77 K Bromis, M Calem, AATS Reinders, SCR Williams, MJ Kempton. Meta-analysis of 89 structural MRI studies in posttraumatic stress disorder and comparison with major depressive disorder. Am J Psychiatry 2018; 175(10): 989–998
https://doi.org/10.1176/appi.ajp.2018.17111199 pmid: 30021460
78 CH Hawkes, K Del Tredici, H Braak. Parkinson’s disease: a dual-hit hypothesis. Neuropathol Appl Neurobiol 2007; 33(6): 599–614
https://doi.org/10.1111/j.1365-2990.2007.00874.x pmid: 17961138
79 S Nigro, R Riccelli, L Passamonti, G Arabia, M Morelli, R Nisticò, F Novellino, M Salsone, G Barbagallo, A Quattrone. Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging. Hum Brain Mapp 2016; 37(12): 4500–4510
https://doi.org/10.1002/hbm.23324 pmid: 27466157
80 RJ Zatorre, M Jones-Gotman. Human olfactory discrimination after unilateral frontal or temporal lobectomy. Brain 1991; 114(Pt 1A): 71–84
pmid: 1998891
81 RL Doty. Olfactory dysfunction in Parkinson disease. Nat Rev Neurol 2012; 8(6): 329–339
https://doi.org/10.1038/nrneurol.2012.80 pmid: 22584158
82 BD Berman, J Smucny, KP Wylie, E Shelton, E Kronberg, M Leehey, JR Tregellas. Levodopa modulates small-world architecture of functional brain networks in Parkinson’s disease. Mov Disord 2016; 31(11): 1676–1684
https://doi.org/10.1002/mds.26713 pmid: 27461405
83 DC Dean 3rd, J Sojkova, S Hurley, S Kecskemeti, O Okonkwo, BB Bendlin, F Theisen, SC Johnson, AL Alexander, CL Gallagher. Alterations of myelin content in Parkinson’s disease: a cross-sectional neuroimaging study. PLoS One 2016; 11(10): e0163774
https://doi.org/10.1371/journal.pone.0163774 pmid: 27706215
84 B Degirmenci, M Yaman, A Haktanir, R Albayrak, M Acar, G Caliskan. The effects of levodopa use on diffusion coefficients in various brain regions in Parkinson’s disease. Neurosci Lett 2007; 416(3): 294–298
https://doi.org/10.1016/j.neulet.2007.02.022 pmid: 17317000
85 SM Smith, M Jenkinson, H Johansen-Berg, D Rueckert, TE Nichols, CE Mackay, KE Watkins, O Ciccarelli, MZ Cader, PM Matthews, TE Behrens. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006; 31(4): 1487–1505
https://doi.org/10.1016/j.neuroimage.2006.02.024 pmid: 16624579
86 A Zalesky. Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection. Magn Reson Imaging 2011; 29(1): 111–125
https://doi.org/10.1016/j.mri.2010.06.027 pmid: 20933352
87 G Nortje, DJ Stein, J Radua, D Mataix-Cols, N Horn. Systematic review and voxel-based meta-analysis of diffusion tensor imaging studies in bipolar disorder. J Affect Disord 2013; 150(2): 192–200
https://doi.org/10.1016/j.jad.2013.05.034 pmid: 23810479
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